=============== <Original Dataset> ===============
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20640 entries, 0 to 20639
Data columns (total 10 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   longitude           20640 non-null  float64
 1   latitude            20640 non-null  float64
 2   housing_median_age  20640 non-null  float64
 3   total_rooms         20640 non-null  float64
 4   total_bedrooms      20433 non-null  float64
 5   population          20640 non-null  float64
 6   households          20640 non-null  float64
 7   median_income       20640 non-null  float64
 8   median_house_value  20640 non-null  float64
 9   ocean_proximity     20640 non-null  object 
dtypes: float64(9), object(1)
memory usage: 1.6+ MB
None

=============== <Modified Dataset> ===============
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20433 entries, 0 to 20432
Data columns (total 9 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   longitude           20433 non-null  float64
 1   latitude            20433 non-null  float64
 2   housing_median_age  20433 non-null  float64
 3   total_rooms         20433 non-null  float64
 4   total_bedrooms      20433 non-null  float64
 5   population          20433 non-null  float64
 6   households          20433 non-null  float64
 7   median_income       20433 non-null  float64
 8   ocean_proximity     20433 non-null  object 
dtypes: float64(8), object(1)
memory usage: 1.4+ MB
None

=============== AutoML Start ===============
=============== Model : Mean shift ===============
best bandwidth = 3.075515672213992
max_iter = 100 / bandwidth = 2 Done.

<Figure size 432x288 with 0 Axes>

========== Compare with original labels ==========
===count===
predict
0.0     9012
1.0     8262
2.0     1739
3.0      109
4.0       70
        ... 
56.0       2
57.0       1
58.0     263
59.0      51
60.0      55
Name: median_house_value, Length: 61, dtype: int64
===max===
predict
0.0     500001.0
1.0     500001.0
2.0     500001.0
3.0     500001.0
4.0     500001.0
          ...   
56.0    343500.0
57.0     58600.0
58.0    500001.0
59.0    500001.0
60.0    500001.0
Name: median_house_value, Length: 61, dtype: float64
===median===
predict
0.0     185700.0
1.0     171700.0
2.0     166000.0
3.0     163300.0
4.0     166100.0
          ...   
56.0    264450.0
57.0     58600.0
58.0    210300.0
59.0    340900.0
60.0    215800.0
Name: median_house_value, Length: 61, dtype: float64
===min===
predict
0.0      14999.0
1.0      14999.0
2.0      22500.0
3.0      53400.0
4.0      47500.0
          ...   
56.0    185400.0
57.0     58600.0
58.0     52800.0
59.0     40000.0
60.0     67900.0
Name: median_house_value, Length: 61, dtype: float64
===mean===
predict
0.0     215848.624723
1.0     198483.938635
2.0     197666.579068
3.0     196085.357798
4.0     188925.742857
            ...      
56.0    264450.000000
57.0     58600.000000
58.0    223505.380228
59.0    312725.568627
60.0    223705.490909
Name: median_house_value, Length: 61, dtype: float64
max_iter = 100 / bandwidth = 3 Done.

<Figure size 432x288 with 0 Axes>

========== Compare with original labels ==========
===count===
predict
0.0     19998
1.0        64
2.0         8
3.0       132
4.0         2
5.0         2
6.0       192
7.0         1
8.0         1
9.0         1
10.0        6
11.0        3
12.0        1
13.0        1
14.0        1
15.0        2
16.0        6
17.0        1
18.0        8
19.0        1
20.0        1
21.0        1
Name: median_house_value, dtype: int64
===max===
predict
0.0     500001.0
1.0     500001.0
2.0     500001.0
3.0     500001.0
4.0     293500.0
5.0     258300.0
6.0     500001.0
7.0     139300.0
8.0     152700.0
9.0     189800.0
10.0    472700.0
11.0    389700.0
12.0    268800.0
13.0    139200.0
14.0    442100.0
15.0    425000.0
16.0    500001.0
17.0    500001.0
18.0    293800.0
19.0    274500.0
20.0    131700.0
21.0    100000.0
Name: median_house_value, dtype: float64
===median===
predict
0.0     180000.0
1.0     159650.0
2.0     158450.0
3.0     190700.0
4.0     222650.0
5.0     235500.0
6.0     161150.0
7.0     139300.0
8.0     152700.0
9.0     189800.0
10.0    248350.0
11.0    124700.0
12.0    268800.0
13.0    139200.0
14.0    442100.0
15.0    259400.0
16.0    166950.0
17.0    500001.0
18.0    219650.0
19.0    274500.0
20.0    131700.0
21.0    100000.0
Name: median_house_value, dtype: float64
===min===
predict
0.0      14999.0
1.0      79900.0
2.0      58600.0
3.0      47500.0
4.0     151800.0
5.0     212700.0
6.0      50000.0
7.0     139300.0
8.0     152700.0
9.0     189800.0
10.0     78300.0
11.0    120200.0
12.0    268800.0
13.0    139200.0
14.0    442100.0
15.0     93800.0
16.0     86600.0
17.0    500001.0
18.0     53100.0
19.0    274500.0
20.0    131700.0
21.0    100000.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0     207356.102660
1.0     192432.828125
2.0     215562.625000
3.0     218996.250000
4.0     222650.000000
5.0     235500.000000
6.0     193827.125000
7.0     139300.000000
8.0     152700.000000
9.0     189800.000000
10.0    261350.000000
11.0    211533.333333
12.0    268800.000000
13.0    139200.000000
14.0    442100.000000
15.0    259400.000000
16.0    251333.666667
17.0    500001.000000
18.0    198337.500000
19.0    274500.000000
20.0    131700.000000
21.0    100000.000000
Name: median_house_value, dtype: float64
max_iter = 100 / bandwidth = 4 Done.

<Figure size 432x288 with 0 Axes>

========== Compare with original labels ==========
===count===
predict
0.0    20141
1.0       28
2.0      236
3.0        3
4.0        1
5.0       23
6.0        1
Name: median_house_value, dtype: int64
===max===
predict
0.0    500001.0
1.0    500001.0
2.0    500001.0
3.0    293500.0
4.0    268800.0
5.0    500001.0
6.0    100000.0
Name: median_house_value, dtype: float64
===median===
predict
0.0    179900.0
1.0    181150.0
2.0    174200.0
3.0    274500.0
4.0    268800.0
5.0    176700.0
6.0    100000.0
Name: median_house_value, dtype: float64
===min===
predict
0.0     14999.0
1.0     93800.0
2.0     47500.0
3.0    151800.0
4.0    268800.0
5.0     58600.0
6.0    100000.0
Name: median_house_value, dtype: float64
===mean===
predict
0.0    207294.332357
1.0    234360.785714
2.0    201834.766949
3.0    239933.333333
4.0    268800.000000
5.0    240939.304348
6.0    100000.000000
Name: median_house_value, dtype: float64
max_iter = 300 / bandwidth = 2 Done.

<Figure size 432x288 with 0 Axes>

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	median_house_value	ocean_proximity
0	-122.23	37.88	41.0	880.0	129.0	322.0	126.0	8.3252	452600.0	NEAR BAY
1	-122.22	37.86	21.0	7099.0	1106.0	2401.0	1138.0	8.3014	358500.0	NEAR BAY
2	-122.24	37.85	52.0	1467.0	190.0	496.0	177.0	7.2574	352100.0	NEAR BAY
3	-122.25	37.85	52.0	1274.0	235.0	558.0	219.0	5.6431	341300.0	NEAR BAY
4	-122.25	37.85	52.0	1627.0	280.0	565.0	259.0	3.8462	342200.0	NEAR BAY
...	...	...	...	...	...	...	...	...	...	...
20635	-121.09	39.48	25.0	1665.0	374.0	845.0	330.0	1.5603	78100.0	INLAND
20636	-121.21	39.49	18.0	697.0	150.0	356.0	114.0	2.5568	77100.0	INLAND
20637	-121.22	39.43	17.0	2254.0	485.0	1007.0	433.0	1.7000	92300.0	INLAND
20638	-121.32	39.43	18.0	1860.0	409.0	741.0	349.0	1.8672	84700.0	INLAND
20639	-121.24	39.37	16.0	2785.0	616.0	1387.0	530.0	2.3886	89400.0	INLAND

	longitude	latitude	housing_median_age	total_rooms	total_bedrooms	population	households	median_income	ocean_proximity
0	-122.23	37.88	41.0	880.0	129.0	322.0	126.0	8.3252	NEAR BAY
1	-122.22	37.86	21.0	7099.0	1106.0	2401.0	1138.0	8.3014	NEAR BAY
2	-122.24	37.85	52.0	1467.0	190.0	496.0	177.0	7.2574	NEAR BAY
3	-122.25	37.85	52.0	1274.0	235.0	558.0	219.0	5.6431	NEAR BAY
4	-122.25	37.85	52.0	1627.0	280.0	565.0	259.0	3.8462	NEAR BAY
...	...	...	...	...	...	...	...	...	...
20428	-121.09	39.48	25.0	1665.0	374.0	845.0	330.0	1.5603	INLAND
20429	-121.21	39.49	18.0	697.0	150.0	356.0	114.0	2.5568	INLAND
20430	-121.22	39.43	17.0	2254.0	485.0	1007.0	433.0	1.7000	INLAND
20431	-121.32	39.43	18.0	1860.0	409.0	741.0	349.0	1.8672	INLAND
20432	-121.24	39.37	16.0	2785.0	616.0	1387.0	530.0	2.3886	INLAND

Main¶